348 results about "Relation graph" patented technology

The invention relates to a water quality prediction method and system based on gating residual enhancement and feature fusion, and belongs to the technical field of water environment intelligent analysis and deep learning. Taking each water quality index as a node of the graph, and constructing two complementary variable relation graph structures by utilizing a Pearson's correlation coefficient and mutual information; respectively inputting the two graph structures into a graph convolutional network, extracting deep dependency features among indexes, and splicing and fusing the deep dependency features. A multi-head attention mechanism is used as a trunk to extract global time dependence, a GRU network is introduced to extract local time sequence features, GRU output is used as an adjustable residual term to be injected into the attention trunk through a residual gating mechanism, self-adaptive enhancement of local dynamic features is achieved, and finally a self-adaptive fusion mechanism is introduced to generate comprehensive representation. According to the method, the complex dependency relationship between the water quality indexes and the time dynamic evolution process can be modeled in a collaborative manner, the response capability to key local change and sudden change events is remarkably enhanced, and the accuracy and robustness of water quality prediction are improved.

The invention relates to the technical field of database management, in particular to a two-way linkage database table and supervision submission form field synchronous construction method which is applied to a supervision data submission scene. According to the scheme, the method comprises the steps that physical structure metadata of a database table and definition metadata of a supervision submission form are obtained, a semantic vector set is formed by combining metadata semantic analysis and semantic coding, and a bidirectional mapping relation graph is constructed through relation weaving; constructing a linkage propagation path according to a field change event, realizing adaptive bidirectional structure adjustment, generating a field synchronous construction scheme, and driving bidirectional mapping relation graph evolution optimization through feedback learning; according to the method, the bidirectional mapping relation graph and self-adaptive bidirectional structure adjustment are creatively combined, and efficient and self-adaptive bidirectional field synchronous construction is realized.

The invention discloses a park management method based on digital twinning, particularly relates to the field of park operation, and is used for solving the problem that a multi-source event is difficult to stably chain under the conditions of compensation delay and evidence gap and causes misalignment of disposal triggering. Comprising the following steps of: registering an event in a pool to generate an event number and writing the event number into a platform arrival moment; screening and mapping an anchor point event to a park object identifier and a process stage identifier to generate a management token; checking a stage sequence and an evidence item requirement set according to an event chain rule table in event chain construction and generating a conflict point list; the delay portrait library generates a credible mark when a source is generated and forms a reverse sequence violation spectrum; the digital twin object relation graph forms an influence domain span mark; the closed table is returned to obtain a disposal convergence guarantee level; the cost-sensitive decision tree outputs a disposal template identifier, and the disposal template table generates a management action instruction and a trigger voucher and records an instruction acceptance identifier; and returning, comparing and freezing the exception management token, and updating the event chain rule table and the delay portrait library.

The invention relates to the technical field of data fusion, and discloses an animal scene-oriented adaptive multi-modal data fusion method, which comprises the following steps of: extracting spatio-temporal characteristics from multi-source heterogeneous data such as visual sense, auditory sense and physiological sensing, constructing an animal-environment-group ternary spatio-temporal relation graph, and constructing an animal-environment-group ternary spatio-temporal relation graph; a pilot frequency sampling problem is solved through an adaptive interpolation algorithm, cross-modal projection alignment is completed in a public semantic space, unified space-time representation is output, and confidence coefficient weight is dynamically calculated based on uncertainty measurement of each modal feature. According to the method, accurate alignment of multi-modal data is realized through the cross-modal space-time attention network, the multi-modal feature alignment error is reduced compared with that of a traditional LSTM method, the training data volume of a federated element migration reinforcement learning framework is reduced compared with that of a traditional migration learning method, and the cross-species generalization performance of the model is improved. A multi-level causal inference engine quantitatively reveals causal association between environmental factors and animal diseases, and in combination with a dynamic decision tree visualization technology, the decision recognition degree is improved.

The invention discloses a network space map surveying and mapping method and system based on multi-source data fusion, and the method comprises the steps: obtaining a multi-source data set in a unified format based on network flow data, equipment information data and geographic position data; obtaining a network asset entity and an incidence relation graph thereof through entity identification and correlation analysis based on the multi-source data set with the uniform format; obtaining a network space three-dimensional map model through three-dimensional space mapping and visual rendering based on the network asset entity and the association relationship map thereof; based on the network space three-dimensional map model, performing dynamic updating according to the accessed real-time data flow to obtain real-time network space situation data; and obtaining a network security risk assessment result through anomaly detection and threat identification based on the real-time network space situation data. According to the invention, visual display and security situation awareness of the network space are realized, and a brand new decision support tool is provided for network security management.

The invention discloses a software development result traceability analysis system based on version control, and relates to the technical field of intelligent software analysis. According to the method, the non-tampering property of code submission records is ensured through a block chain technology, a credible basis is provided for traceability, the code semantic analysis module generates a data set containing semantic association in combination with a clustering algorithm and weighted calculation, and the function influence positioning module constructs a function association graph by applying a graph neural network algorithm, so that the traceability is improved. An influence path of code change on system functions is automatically identified, the time cost of complex project function influence positioning is remarkably reduced, an evolution path generation module analyzes and tracks a code evolution path in combination with a time sequence, and a core module identification module clusters key nodes and positions a core function module. And the time-tracing source data storage module generates a visual relation graph and a queried data set, so that the whole-process management from code submission to result tracing is realized.

The invention discloses a risk assessment method and system based on topology analysis, and relates to the technical field of risk assessment, and the method comprises the following steps: analyzing a communication relation between nodes, extracting a plurality of propagation paths between any two nodes, and forming a path set; mapping the path set based on the risk transmission direction and the path attribute, and constructing a multi-path propagation relation graph; based on the multi-path propagation relation graph, identifying a risk aggregation node with multi-path risk input, and fusing multi-path attributes to calculate a comprehensive risk value of the node; and generating network risk distribution based on the comprehensive risk value in combination with the operation state data. According to the method, the multi-path propagation topology model is constructed, and the risks on multiple paths are subjected to convergence analysis and comprehensive quantification in combination with the node operation state and the path interaction characteristics, so that the problem that the overall risk of the network is underestimated only by depending on a single dominant path in the prior art is solved.

The invention discloses a financial knowledge graph construction method and system based on artificial intelligence, and relates to the field of artificial intelligence data processing. The method comprises the following steps: performing multi-dimensional semantic analysis on a heterogeneous financial data source, and extracting a structured semantic fragment; constructing a financial entity perception unit, identifying a multi-granularity entity and generating a unique code; generating a preliminary relation graph based on the event cascade relation and the attachment structure, and injecting a semantic translation label; normalizing the atlas relationship through semantic separation and a label reconstruction mechanism to form a financial relationship network with consistent semantics; executing evolution increment iteration in combination with the newly added corpus, and dynamically updating nodes and edge sets; and performing semantic consistency and structural integrity evaluation on an iteration result, and outputting a stable financial knowledge graph structural body. By introducing a multi-factor semantic analysis model, a causal relationship modeling mechanism and a graph evolution iteration strategy, systematic improvement of the financial knowledge graph in the aspects of structural expression precision, semantic reasoning ability and dynamic adaptability is achieved.

The invention relates to the technical field of artificial intelligence, and provides a subway network traffic prediction method based on association modeling, comprising: acquiring a heterogeneous data source of a target subway network; the heterogeneous data sources are cleaned, aligned and fused, and a time-space association data set is constructed; based on the subway network topology and the real-time passenger flow state, constructing a dynamic relation graph representing the dynamic interaction between the line and the station; the space-time correlation data set and the dynamic relation graph are utilized to cooperatively train a space-time prediction module and a relation reasoning module in an alternate optimization mode, and the relation reasoning module iteratively updates an edge weight in the dynamic relation graph through a graph attention mechanism and a space-time convolution operation; and based on the dynamic relation graph and the optimized space-time prediction module, carrying out multi-step prediction on the passenger flow in the future period and outputting a prediction uncertainty quantitative index. According to the technical scheme of the application, the accuracy and reliability of subway passenger flow prediction are significantly improved by fusing multi-source data and dynamically modeling the site association relationship.

The invention discloses a scientific and technological achievement analysis and prediction method and system based on big data, and relates to the technical field of machine learning and big data analysis, and the method comprises the steps: collecting and preprocessing multi-source scientific and technological achievement semantic data, and constructing a scientific and technological concept relation graph; the method comprises the following steps: performing training by taking a time sequence diagram convolutional network as a basic framework and taking a scientific and technological concept relation graph as a training sample, constructing a dynamic knowledge flow semantic model, performing evolution feature extraction on the scientific and technological concept relation graph by utilizing the dynamic knowledge flow semantic model, and outputting a knowledge flow feature vector; and inputting the causal enhanced space-time diagram into a space-time diagram neural network, aggregating semantic association and causal relationships among the scientific and technological achievements in a space dimension, capturing a dynamic change mode of scientific and technological achievement characteristics in a time dimension, and outputting a scientific and technological concept time sequence predicted value sequence. According to the method, the causal enhancement space-time diagram is constructed, so that trend deduction and causal traceability analysis are carried out for the time dimension, and the accuracy of scientific and technological achievement development trend prediction is improved.

The invention discloses a software development project progress prediction management method based on artificial intelligence, and relates to the technical field of project management, and the method comprises the steps: extracting a causal relation from entity relation graph data, and constructing a causal knowledge graph; a graph structure analysis method and a time sequence prediction method are combined, predicted completion time and delay probability are calculated for the causal knowledge graph and the associated time sequence data, and a progress risk assessment result is generated; performing causal chain identification on the progress risk assessment result and the causal knowledge graph by adopting a causal reasoning method to generate delay interpretation information; according to the delay interpretation information, a preset resource priority and a task weight, a rule driving and priority scheduling strategy is adopted to construct a compensation scheduling scheme; and implementing a compensation scheduling scheme, collecting implementation effect data and analyzing a scheduling effect through a closed-loop feedback and dynamic adjustment mechanism, and generating an optimized scheduling management scheme. The intelligent level and the practical value of project progress management are greatly improved.

The invention relates to the technical field of knowledge graph completion, provides an implicit relation perception time sequence knowledge graph completion method based on dynamic embedding and self-attention, and aims to improve the inference and completion capability of missing facts in a time sequence knowledge graph. According to the method, time evolution modeling, a graph neural network and semantic similarity calculation are combined, and dynamic embedding representation fusing static, trend and periodic characteristics is constructed. Explicit structure information is extracted through a multilayer relational graph convolutional network, and meanwhile, an implicit semantic similarity relationship under synchronous and asynchronous time is introduced to construct a sparse semantic graph. Structural information and semantic information are fused through GRU, multi-time step features are aggregated by adopting a time perception self-attention mechanism, and key time information is highlighted. And finally, entity prediction is completed by using a ConvTransE decoder, and the model is optimized through cross entropy loss. According to the method, a static structure and implicit semantics can be modeled at the same time, the time sensitivity is enhanced, and the method is suitable for large-scale dynamic graph completion and has better reasoning ability and generalization performance.

The invention discloses a recommendation system-oriented high-concealment poisoning attack detection method and application, and the method comprises the following steps: S1, user behavior and relationship modeling: constructing a user feature vector and symbiotic relationship graph, and describing user scoring behavior preference and a co-occurrence relationship; s2, importance pre-screening: based on similarity measurement and importance modeling of score distribution, filtering out normal users weakly related to potential attack users; s3, cross-graph relation decoupling: carrying out key relation extraction and dynamic and static relation separation on the user relation graph, and obtaining high-quality relation representation through a cross-graph fusion mechanism; and S4, double-hyper-sphere cooperative detection: normal user representation is restrained by using a concentric hyper-sphere shell, and abnormal user detection is realized through the degree of deviation from the boundary. According to the method, high-concealment poisoning attacks can be effectively detected in a real recommendation system environment, the detection accuracy is remarkably improved, the false alarm rate is reduced, and the method has good practicability and robustness.

The invention belongs to the technical field of fan arrangement, and discloses a fan point location automatic arrangement method and system based on artificial intelligence, and the method comprises the steps: firstly receiving a plurality of spatial constraint layers and deployment parameters, and unifying the coordinates; a conflict relation graph is constructed based on the processed constraint layers, nodes of the graph are different constraint layers, and edges are conflict strength among constraints; inputting the map into a pre-training map attention network, and reasoning to obtain a conflict slow-release factor which quantifies a constraint relaxation degree; then the factors are fed back to a point location search algorithm, and candidate point locations are evaluated by using a scoring function of the fusion factors; and finally, outputting a final arrangement scheme according to a scoring result. According to the method, through artificial intelligence middleware, such as a graph attention network, an artificial intelligence-based search algorithm and the like, the problem of excessive region deletion of a traditional method is solved, and the area and arrangement reasonability of a deployable region are improved.

The invention discloses an alarm intelligent analysis and noise reduction verification method, device and equipment based on an AI large model, and relates to the technical field of alarm intelligent analysis, and the method comprises the following steps: obtaining alarm data and environment context information, constructing a system relation graph, and generating a multi-dimensional feature vector set based on the system relation graph; based on the multi-dimensional feature vector set, obtaining causal intensity among the alarm events to obtain a causal intensity matrix, and mapping the causal intensity matrix to a system relation graph to form an alarm causal association graph; performing graph structure clustering and screening on the alarm causal association graph to obtain a causal cluster; performing root cause alarm identification and clustering compression according to the causal clustering cluster to obtain compressed output of redundant alarms; according to the invention, high-precision causal identification and adaptive clustering of multi-source alarm events are realized, repeated alarms and false alarms are effectively reduced, and the root cause analysis and noise reduction efficiency is improved.

The invention discloses an agent configuration method and device based on front-end interaction and a medium, and relates to the technical field of computers.The method comprises the steps that a user interaction behavior is received through a front-end interaction interface, the user interaction behavior is analyzed, and a corresponding response instruction is generated; executing the response instruction, creating a plurality of agent nodes, and configuring corresponding role information; determining a task dependency relationship between the agent nodes, and constructing an agent node relationship graph through a preset task execution rule; receiving and analyzing a to-be-executed task, extracting required agent role information, determining to-be-executed agent nodes, and determining a to-be-executed sequence corresponding to the to-be-executed agent nodes according to the agent node relation graph; and calling the agent models corresponding to the to-be-executed agent nodes in sequence, executing the to-be-executed task, and updating the state parameters. Through deep fusion of front-end visual interaction and automatic process arrangement, the intuition and efficiency of the configuration process are improved.

The invention relates to the technical field of data compliance. According to the AI-driven data compliance rule intelligent method, device and equipment and the medium, the method comprises the steps that natural language input of a user is obtained, and user demand description is obtained; performing multi-dimensional semantic analysis on the user demand description through a pre-trained large language model, and generating a structured semantic framework comprising an intention vector, an entity relation graph and a constraint condition set; performing compliance correction on the structured semantic framework to generate an optimized semantic framework; performing logic conflict detection and performance simulation test on the domain-specific language rule to generate an executable compliance rule; and deploying the executable compliance rule to the target data governance platform so as to achieve the technical effects of reducing the learning threshold and the manual error rate generated by the domain specific language rule, improving the semantic adaptation capability in a complex business scene and optimizing the operation stability of the rule on the data governance platform.

The invention discloses an intelligent manufacturing feeding method and system fusing three-dimensional vision and robot cooperation, and belongs to the technical field of robot automation control. Three-dimensional point cloud data in a feeding area is acquired, and a workpiece space topological relation graph is constructed through adaptive clustering and boundary extraction; generating a task association tensor in combination with the physical attribute of the workpiece and a scheduling rule; the method comprises the following steps: collecting operation state information of a plurality of industrial robots, and constructing an action capability tensor; based on the task association tensor and the action capability tensor, an optimal task allocation matrix is generated through a multi-target collaborative optimization algorithm, and dynamic matching between the robot and the workpiece is achieved; in combination with real-time point cloud feedback, the initial path is corrected and the tail end is adjusted, and the robot is driven to accurately execute a grabbing task; the intelligent feeding system has the advantages of being high in environment adaptability, high in dispatching intelligence degree, excellent in path control precision and the like, and is suitable for high-dynamic complex manufacturing scenes.

The invention discloses an industrial design-oriented drawing semantic analysis and structured conversion method and system. The method comprises the following steps: acquiring an input image, carrying out different-scale coding on image features through an image coding backbone network, selecting a specific layer to extract a multi-scale feature map, and fusing to generate multi-scale features; inputting the multi-scale features into a geometric primitive detection network and an other element detection network, respectively identifying geometric primitives and non-geometric primitives, and carrying out positioning and classification; analyzing mutual relations between geometric elements and other elements contained in the graph through an element relation graph network, and generating formalized language description based on a matching rule; a Prompt template is constructed, geometric information is supplemented by using a multi-modal large model, the rationality of the supplemented information is verified through a geometric attribute relationship verifier, and complete formalized language description is obtained. According to the method, the industrial drawing image containing the complex constraint relation can be efficiently and accurately converted into machine-readable structured data.

The invention relates to a weld defect intelligent identification method and system based on multi-modal fusion. The method comprises the following steps: acquiring multi-modal data of the same welding seam area to obtain a multi-modal image set, enhancing a feature map of each modal image in the multi-modal image set through a preset feature extraction network to obtain an enhanced feature, and extracting a multi-scale depth feature of the enhanced feature; on this basis, cross-modal feature alignment is realized by using deformable convolution, and a physical constraint mechanism is introduced to generate a fusion feature map; candidate defect areas are generated based on the fusion features, feature vectors and position information of the candidate defect areas are extracted, and a defect relation graph is constructed; relational reasoning optimization feature representation is carried out through a graph neural network, collaborative judgment of defect types, positions and incidence relations is achieved, the identification accuracy of a symbiotic defect group in a complex industrial scene is remarkably improved, physical relevance between defects is reliably quantified, and the anti-interference capacity is enhanced.

The invention relates to the technical field of data mining, in particular to an enterprise financial data analysis and management cloud platform system and a programming method thereof. The method comprises the following steps: collecting multi-source enterprise financial data, and carrying out analysis pre-classification and compression coding to obtain a tense data collection event stream; performing context sensitive semantic alignment based on the temporal data acquisition event stream to obtain a high-dimensional semantic tensor; performing external economic entity semantic constraint according to the high-dimensional semantic tensor to obtain a financial entity relation graph; performing structured chain packaging and consensus verification on the logic relationship among the nodes of the financial entity relationship graph to obtain an encrypted verification financial chain packet; and performing programming logic constraint on the encrypted verification financial chain packet to obtain a pre-constructed logic execution unit set, and uploading the pre-constructed logic execution unit set to the enterprise private cloud platform to execute a platform construction task. According to the invention, the intelligent level and the management efficiency of enterprise financial analysis management are enhanced.

The invention provides a product recommendation method and device, electronic equipment, a medium and a program product, and can be applied to the technical field of artificial intelligence, the technical field of big data, the technical field of block chains and the technical field of privacy computing. The method comprises the following steps: acquiring structured data and unstructured data of a target user; extracting preference features based on the unstructured data, and obtaining a time sequence preference portrait by using a time sequence attention model; obtaining a target user portrait based on the time sequence preference portrait and the structured data; obtaining a multi-dimensional sequential relation graph, and reasoning the multi-dimensional sequential relation graph by using a graph neural network to obtain relation representation; executing collaborative filtering scoring based on the target user portrait and the relationship representation, and generating an intermediate product score; taking the generated intermediate product score as an input, and outputting a candidate product score based on a recommendation optimization function containing a space-time weight; and performing product recommendation based on the candidate product scores.

The invention relates to the technical field of price monitoring, in particular to a building material multi-source price anomaly detection method, which comprises the following steps: firstly, uniformly metering and pricing calibers, learning a conversion coefficient, and constructing a replaceable relation graph; multi-source distribution is aligned through optimal transmission, residual errors and shadow prices are obtained based on structure invariants and variational inequality, and abnormal evidences are formed through hypergraph propagation and persistent coherence; generating a valence band reference in combination with a convex hull method and distribution robust optimization under the equilibrium clearing of graph regularization; a feasible set is defined by price bands and constraints, a weighted maximum satisfactory model is constructed to position a minimum default set, a minimum correction suggestion is generated by using vector optimal transmission and packet sparsity, and an executable closed loop is realized through satisfactory model theory verification.

The invention provides a supply chain transaction credit data risk management method and device, and belongs to the technical field of supply chain management. The method provided by the invention comprises the following steps: acquiring credible data; verifying the authenticity of the credible data, performing cleaning, standardization and association processing on the verified data, and constructing a multi-dimensional feature variable; based on the multi-dimensional characteristic variables, through relation graph analysis, machine learning model operation and rule engine judgment, fraud identification, credit scoring, credit line calculation and differentiated interest rate pricing are carried out, and a comprehensive risk assessment result is output; taking the comprehensive risk assessment result as a reference, monitoring key links of supply chain transactions in real time, comparing latest transaction data with the reference, triggering corresponding early warning and automatic disposal actions according to an abnormal level, and recording a disposal process and a result to form feedback data; and comparing the evaluation feedback data with a historical prediction result, and performing iterative optimization on the machine learning model and the multi-dimensional feature variable construction process by using the feedback data.

System and method for aspect-level sentiment classification. The system includes a computing device, the computing device has a processer and a storage device storing computer executable code. The computer executable code is configured to: receive an aspect term-sentence pair; embed the aspect term-sentence pair; parse the sentence using multiple parsers to obtain dependency trees, and perform edge union to obtain a merged graph; combine the embedding and the merged graph to obtain a relation graph; perform a relation graph neural network on the relation graph; extract hidden representation of the aspect term from updated relation neural network; and classify the aspect term based on the extracted representation to obtain a predicted classification label of the aspect term. During training, the computer executable code is further configured to calculate a loss function based on the predicted label and the ground truth label, and adjust parameters of models.

The invention discloses a scene interaction perception method for cross-modal visual relation detection and related equipment, relates to the technical field of scene interaction perception, and aims to detect spatiotemporal characteristics through a visual relation detection model, establish triple data of people, objects and behaviors in a scene, namely a three-dimensional relation graph, and accurately identify an interaction state between a customer and a service facility. Through a score correction matrix of priori knowledge and a gating fusion mechanism, a statistical rule and a real-time detection result are deeply fused, the data driving advantage in traditional safety detection is inherited, an explainable path of knowledge reasoning is introduced, the model has generalization ability for a novel abnormal mode while the false alarm rate is reduced, and the safety detection accuracy is improved. And on the basis of a preset encoder group obtained by comparative learning joint training and a gating fusion mechanism, redundant information is effectively suppressed, so that the semantic conflict is eliminated while the complementary information is reserved by the multi-modal features, and the accuracy of abnormal behavior detection in a customer service scene is improved.

The invention discloses a zero-sample composite fault diagnosis method based on semantic graph embedding and multi-stage fusion, and the method comprises the following steps: firstly, generating an initial semantic descriptor through manual definition or statistical features according to known fault types, and constructing a fault relation structure diagram to represent the association between types; combining reconstruction, semantic comparison and propagation loss by using a graph convolutional network, and fusing the initial semantics and the relation graph to generate enhanced semantic features; meanwhile, a multi-modal model is constructed to extract vibration, temperature, acoustics and other signal features, and after multi-stage fusion of input-stage cross-modal attention, feature-stage Transform and output-stage semantic alignment, a semantic feature supervision training network is jointly enhanced; and finally, extracting unseen fault features in a zero sample scene and carrying out classified diagnosis. According to the method, through a multi-modal fusion and semantic enhancement strategy, the precision and generalization ability of zero-sample composite fault diagnosis are remarkably improved.

The invention discloses a multi-platform log anomaly recognition and analysis method based on self-supervised learning, which comprises the following steps: collecting and preprocessing multi-platform data, constructing a call chain relation graph, and generating a standardized log sequence; carrying out conversational splicing to generate a conversational log sequence; inputting the conversational log sequence into an extended LogBERT model, and generating a multi-granularity log representation vector; cross-platform domain alignment processing is carried out through adversarial training based on gradient inversion and cross-platform comparison alignment, and cross-platform log representation in the unified embedding space is generated; an abnormal detection result is determined by adopting a one-class classification detection method and confidence interval calibration; generating a root cause analysis result by utilizing a graph attention mechanism and time sequence correlation analysis; and generating an exception report according to an exception detection result and a root cause analysis result. According to the method, the accuracy of multi-platform log anomaly detection and the precision of root cause analysis are improved, and the method is suitable for cross-platform operation and maintenance and monitoring scenes.

The invention discloses an event entity prediction method based on perceptual contrast learning, which comprises the following steps: obtaining a time sequence knowledge graph of all events, each event being represented by a subject, a relationship, an object and a time tetrad; performing reverse operation on the tetrad to obtain a reverse tetrad, and respectively constructing a global historical graph and a local historical graph for all events in any day; then constructing an event entity prediction model, and inputting the global historical graph and the local historical graph into the event entity prediction model to train the model; and finally, predicting the event entity by using the trained event entity prediction model. According to the method, the accuracy of event entity prediction is improved through multi-tense dynamic embedding and relational graph comparative learning, the influence of time on event evolution can be more accurately captured by adopting the multi-tense dynamic embedding, and a dynamic context is provided for local historical node feature updating; the problem that the flexibility is insufficient due to the fact that event prediction excessively depends on recent events is solved.

The invention belongs to the technical field of computer big data, and discloses a relation graph construction and layout method, device and system based on spectral clustering and a storage medium, a clustering center is initialized through a genetic algorithm, the clustering center serves as genetic information and is coded into a character string, the operation time can be shortened, and the classification precision can be improved; furthermore, a weighted Euclidean distance is constructed as a distance function of a K-means algorithm, mutual relation weighting between the features can be reflected, features of different weights are counted into the distance, the classification precision can be effectively improved, the loss is reduced, and the classification efficiency is improved. According to the method, an initial similarity matrix, obtained through a traditional similarity calculation method, between XML documents is corrected through an affinity propagation algorithm, the similarity between the hidden similar XML documents can be reflected, on the basis, the correct clustering number and the correct clustering result are obtained by applying a multi-path spectral clustering method NJW, the method is irrelevant to the sequence of the XML documents, and the method has the advantages of being high in practicability and easy to popularize. The method is suitable for clustering the retrieval results of the XML documents arranged in any sequence.